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Record W2124371531 · doi:10.1155/2011/679492

Chemiluminescent Nanomicelles for Imaging Hydrogen Peroxide and Self‐Therapy in Photodynamic Therapy

2011· article· en· W2124371531 on OpenAlexaff
Rui Chen, Luzhong Zhang, Jian Gao, Wei Wu, Yong Hu, Xiqun Jiang

Bibliographic record

VenueBioMed Research International · 2011
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsPhotodynamic therapyChemiluminescenceHydrogen peroxideMedicineChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrogen peroxide is a signal molecule of the tumor, and its overproduction makes a higher concentration in tumor tissue compared to normal tissue. Based on the fact that peroxalates can make chemiluminescence with a high efficiency in the presence of hydrogen peroxide, we developed nanomicelles composed of peroxalate ester oligomers and fluorescent dyes, called peroxalate nanomicelles (POMs), which could image hydrogen peroxide with high sensitivity and stability. The potential application of the POMs in photodynamic therapy (PDT) for cancer was also investigated. It was found that the PDT-drug-loaded POMs were sensitive to hydrogen peroxide, and the PDT drug could be stimulated by the chemiluminescence from the reaction between POMs and hydrogen peroxide, which carried on a self-therapy of the tumor without the additional laser light resource.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.304
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2011
Admission routes1
Has abstractyes

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